Borrowing it
Nothing to install: this file belongs to alfadur7/llm-wiki-newsroom. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/alfadur7/llm-wiki-newsroom/main/.claude/skills/consulting-writing/SKILL.mdgit clone --depth 1 https://github.com/alfadur7/llm-wiki-newsroomWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/alfadur7/llm-wiki-newsroom/consulting-writing)<a href="https://agentmods.dev/skills/alfadur7/llm-wiki-newsroom/consulting-writing"><img src="https://agentmods.dev/badge/skills/alfadur7/llm-wiki-newsroom/consulting-writing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/alfadur7/llm-wiki-newsroom/consulting-writing"><img src="https://agentmods.dev/badge/skills/alfadur7/llm-wiki-newsroom/consulting-writing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00091 | $0.01012 |
| Opus 5 | $0.00046 | $0.00506 |
| Sonnet 5 | $0.00018 | $0.00202 |
| Haiku 4.5 | $0.00009 | $0.00101 |
Grade A, and why
consulting-writing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 34 lines — stays where its author put it; the contents beside it link to each section on GitHub.
consulting-writing
Writing craft drawn from management-consulting deliverables — the executive-summary / pyramid structure that compresses a complex domain for decision-makers, and the MECE completeness of a roll-up. criteria.json is the SoT for each criterion's definition, comparator, and source; shared parsing for the deterministic checks is orchestrator-injected (the skill is content-type-agnostic). Examples are illustrative of the target English prose.
MECE completeness (con.mece-axes · con.mece-clusters)
A roll-up document must cover its subitems with no overlap and no gaps — the MECE principle separates a set of items into subsets that are "mutually exclusive (ME) and collectively exhaustive (CE)." Group many items under 2–4 top axes, place every item under at least one axis, and put anything that fits nowhere into an explicit residual (기타 / "other") axis. Every subunit (cluster, theme, …) must appear in the roll-up (collectively exhaustive — no subunit dropped).
Executive Summary structure (con.scr · con.so-what · con.bold-bullet · con.numeric-precision)
The intro craft that compresses a complex domain for a decision-maker. Resists deterministic measurement (judge=M, qualitative review); the source techniques shared by author and reviewer:
- SCR (con.scr) — develop the intro as Situation → Complication → Resolution (McKinsey). e.g. ✅ "Market share fell 8% year over year (Situation). A competitor entered at a 15% lower price (Complication). We respond by repositioning as premium (Resolution)." / ❌ "The market environment is difficult and improvement is needed" (Situation·Complication·Resolution undifferentiated)
- So-what upfront (con.so-what) — lead with the conclusion (the key implication), not a long description or preamble. e.g. ✅ "We can cut indirect costs by ₩2B per year — labor, supply waste, and process inefficiency are the drivers" (conclusion first) / ❌ "We analyzed three factors affecting profitability…" (description first)
- Bold-bullet (con.bold-bullet) — build metric runs as a bold key-claim heading + supporting bullets, so the scannable takeaway is the bold line (BCG executive-summary standard: lead with the "so what", evidence below). e.g. ✅ "Market consolidation drives customer acquisition cost up 34% per year — Q2 three-way merger shrinks the vendor pool / pressure on the procurement team's volume discounts" (bold conclusion + grounds) / ❌ "The market is changing / competitors are consolidating / customers want better prices" (flat bullets, no conclusion)
- Numeric precision (con.numeric-precision) — support claims with concrete numbers (amount·date·%·proper noun) instead of vague quantifiers. e.g. ✅ "Revenue grows 12% through Q3 2026, adding ₩4.5B" / ❌ "Revenue will rise significantly going forward" (abstract quantifier)
- Numeric density (con.numeric-density) — do not overpack a paragraph with numbers; isolate key numbers in bold-bullets so they are not buried (deterministic measurement — per-paragraph number-token ceiling, threshold injected by manifest).
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 34 lines · 91 tokens per session scan A 76cdbbc279b8
consulting-writing is a skill published in the GitHub repository alfadur7/llm-wiki-newsroom (85 stars, last pushed yesterday), licensed MIT. It adds 91 tokens to every session and 1,012 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
binder-modeling
Binder data modeling — define entity types, fields, relations, constraints, views, and navigation. Use when asked to "create a type", "add a field", "define a schema", "set up relations", "model entities", "create a view", "set up navigation", "render entities as files", or design a binder workspace schema.
binder-app
Build a small HTTP app (dashboard, kanban board, admin panel, custom UI) on top of an existing Binder workspace. Use when asked to "create an app", "build a dashboard", "show records in a chart", "make a kanban/board", "build an admin panel", or "add a UI on top of binder".
binder-import
Import external data into a Binder workspace. Handles CSV, JSON, YAML, Markdown files, and directories of Markdown. Use when asked to "import data", "load records from a file", "ingest documents", "migrate data into binder", or bulk-create records from an external source.
binder-cli
Binder CLI for knowledge graph operations — CRUD, search, schema inspection, transaction import, docs rendering. Use when asked to "query binder", "search records", "create a record", "check the schema", "import transactions", "undo changes", or work with a binder workspace.
capture-task
Capture a new task — create a draft or pending Task record from a rough idea or detailed spec. Use when asked to "add a task", "log a bug", "create a task", or "add to backlog".
refine-backlog
Refine the backlog — screen drafts, remove duplicates, fill in missing details, classify, link, and move to pending.